To ensure the accuracy of registration, the feature points should be highly stable and repetitive between the image pair. For multi-source remote sensing images, the difference between scale, blur, intensity, or content details can lead to instability of local features. The feature point detection in SIFT and FAST algorithms is too sensitive to the intensity. Harris corner detection [1]} focuses on locating obvious 'corner' structure, which is distinct and stable even in multi-model images. Fig. 2 shows the feature point detection results of the three methods in an optical-infrared image pair. By comparison, the feature points obtained by the Harris corner detector are the most highly repetitive. Harris corner detector also has the advantages of strong robustness to local noise and high computation efficiency. Therefore, the Harris corner detector is adopted to extract feature points from multi-source remote sensing images.
